Designing water uptake Although the locations of water molecules in some porous materials have been determined with diffraction techniques, determining the filling sequence of water sites has been challenging. Hanikel et al . used single-crystal x-ray diffraction to locate all of the water molecules in pores of the metal-organic framework MOF-303 at different water loadings (see the Perspective by Öhrström and Amombo Noa). They used this information on the water molecule adsorption sequence to modify the linkers of this MOF and control the water-harvesting properties from humid air for different temperature regimes. —PDS

Predicting and understanding the chemical bond is one of the major challenges of computational quantum chemistry. Kohn−Sham density functional theory (KS-DFT) is the most common method, but approximate density functionals may not be able to describe systems where multiple electronic configurations are equally important. Multiconfigurational wave functions, on the other hand, can provide a detailed understanding of the electronic structure and chemical bond of such systems. In the complete-active-space self-consistent field (CASSCF) method one performs a full configuration interaction calculation in an active space consisting of active electrons and active orbitals. However, CASSCF and its variants require the selection of these active spaces. This choice is not black-box; it requires significant experience and testing by the user, and thus active space methods are not considered particularly user-friendly and are employed only by a minority of quantum chemists. Our goal is to popularize these methods by making it easier to make good active space choices. We present a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules. The protocol shows high prediction performance for a given target system as long as a properly correlated system is chosen for training. Good active spaces are correctly predicted with a considerably better success rate than random guess (larger than 80% precision for most systems studied). Our automated machine learning protocol shows that a "black-box" mode is possible for facilitating and accelerating the large-scale calculations on multireference systems where single-reference methods such as KS-DFT cannot be applied.

Thiourea was used as an additive in the iodide/iodine redox electrolyte for dye-sensitized solar cell and its effect was investigated. Thiourea was found to have the simultaneous effect of a positive band edge shift and a decrease in charge recombination rate. Addition of 0.05 M thiourea in the electrolyte comprising 0.7 M 1-methyl-3-propylimidazolium iodide (MPII) and 0.05 M I2 in acetonitrile enhanced significantly photocurrent density from 7.7 to 10.8 mA/cm2, while voltage decreased from 0.78 to 0.71 V. As a result, overall conversion efficiency increased from 4.7% to 5.8%, corresponding to increment of 23%. The solution acidity was changed from pK a = 18.9 (thiourea in acetonitrile) to pK a = 4.9 (thiourea in iodide- and iodine-containing acetonitrile), corresponding to change in pH from 10.1 to 3.1, which was attributed to chemical reaction between thiourea and iodine. As a consequence of the reaction, protons were produced and triiodide concentration was slightly reduced. The generation of protons in the electrolyte, associated with a positive shift of conduction band edge, led to a significant increase in photocurrent density. The unexpectedly small voltage drop, however, was ascribed to a slow recombination rate due to the reduced triiodide concentration. A large increase in photocurrent density along with a small decrease in voltage was also demonstrated from the variation of thiourea concentration.

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<div>Predicting and understanding the chemical bond is one of the major challenges of computational quantum chemistry. Kohn−Sham density functional theory (KS-DFT) is the most common method, but approximate density functionals may not be able to describe systems where multiple electronic configurations are equally important. Multiconfigurational wave functions, on the other hand, can provide a detailed understanding of the electronic structure and chemical bond of such systems. In the complete-active-space self-consistent field (CASSCF) method one performs a full configuration interaction calculation in an active space consisting of active electrons and active orbitals. However, CASSCF and its variants require the selection of these active spaces. This choice is not black-box; it requires significant experience and testing by the user, and thus active space methods are not considered particularly user-friendly and are employed only by a minority of quantum chemists. Our goal is to popularize these methods by making it easier to make good active space choices. We present a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules. The protocol shows high prediction performance for a given target system as long as a properly correlated system is chosen for training. Good active spaces are correctly predicted with a considerably better success rate than random guess (larger than 80% precision for most systems studied). Our automated machine learning protocol shows that a “black-box” mode is possible for facilitating and accelerating the large-scale calculations on multireference systems where single-reference methods such as KS-DFT cannot be applied.</div>

We report the synthesis and characterization of the first plutonium metal-organic framework (MOF). Pu-UiO-66 expands the established UiO-66 series, which includes transitionmetal, lanthanide, and early actinide elements in the hexanuclear nodes. The thermal stability and porosity of Pu-UiO-66 were experimentally determined and multi-faceted computational methods were used to corroborate experimental values, examine inherent defects in the framework, and decipher spectroscopic signatures. The crystallization of a plutonium chain side product provides direct evidence of the competition that occurs between modulator and linker in MOF syntheses. Ultimately, the synthesis of Pu-UiO-66 demonstrates adept control of Pu(IV) coordination under hydrolysis-prone conditions, provides an opportunity to extend trends across isostructural UiO-66 frameworks, and serves as the foundation for future plutonium MOF chemistry. http://pubs.acs.org. More details on synthetic materials and methods, computational methods, characterization by SEM-EDS, XRD, TGA, FT-IR, Raman, crystallographic data, and manual indexing (PDF)

Using grand canonical Monte Carlo simulations, computational screening of hundreds of pure silica zeolites were conducted to identify materials that show enhanced CO 2 uptake under humid conditions. Herein, we show that CO 2 adsorption performance can be either enhanced or degraded depending on the CO 2 /H 2 O binding site separations and characteristics of CO 2 −H 2 O interaction energies. As expected, CO 2 adsorption capacity is significantly degraded when its binding sites overlap with the H 2 O sites. On the other hand, CO 2 adsorption performance is enhanced when CO 2 /H 2 O binding sites are clearly separated as shown from the molecular simulations. However, we show that there are zeolite structures where CO 2 enhancement is observed despite the close distance between the CO 2 and H 2 O binding sites. It is demonstrated that favorable long-range Coulomb interaction between CO 2 and H 2 O molecules is responsible for enhanced CO 2 adsorption performance in these materials.

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